cgmanalyserDTW

========= loading data =========

m1 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID01.csv')
m2 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID02.csv')
m3 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID03.csv')
m4 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID11.csv')
m5 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID12.csv')
m6 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID13.csv')
m7 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID21.csv')
m8 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID22.csv')
m9 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID23.csv')
m10 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID29.csv')
m11 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID30.csv')
m12 = read.csv('~/Desktop/NCSA/CGManalyzer-datasets/ID31.csv')

training_frame = rbind.data.frame(
                            m1$glucoseValue,
                            m2$glucoseValue,
                            m3$glucoseValue,
                            m4$glucoseValue,
                            m5$glucoseValue,
                            m6$glucoseValue,
                            m7$glucoseValue,
                            m8$glucoseValue,
                            m9$glucoseValue,
                            m10$glucoseValue,
                            m11$glucoseValue,
                            m12$glucoseValue
                            )
training_frame

========= loading packages ===========

require(dtwclust)

Loading required package: dtwclust
Loading required package: proxy

Attaching package: ‘proxy’

The following objects are masked from ‘package:stats’:

    as.dist, dist

The following object is masked from ‘package:base’:

    as.matrix

Loading required package: dtw
Loaded dtw v1.21-3. See ?dtw for help, citation("dtw") for use in publication.

Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     
Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
dtwclust:
Setting random number generator to L'Ecuyer-CMRG (see RNGkind()).
To read the included vignettes type: browseVignettes("dtwclust").
See news(package = "dtwclust") after package updates.

require(mcclust)

Loading required package: mcclust
Loading required package: lpSolve

require(ClusterR)

Loading required package: ClusterR
Loading required package: gtools

======== HIERARCHICAL => Raw ===========

clust.hier_raw <- tsclust(training_frame, type = "h", k = 4L, distance = "dtw2", trace=TRUE, control = hierarchical_control(method = "ward.D"))

Calculating distance matrix...
Performing hierarchical clustering...
Extracting centroids...

    Elapsed time is 114.916 seconds.

plot(clust.hier_raw, type="sc")

plot(clust.hier_raw)

t(cbind(training_frame[,0], cluster = clust.hier_raw@cluster))

        1 2 3 4 5 6 7 8 9 10 11 12
cluster 1 1 2 3 3 3 1 4 3  4  4  4

l_hier <- clust.hier_raw@cluster
m_hier <- c(1,1,1,3,3,3,2,2,2,4,4,4)

plot(range(1:12),range(1:4), type='n')
points(m_hier, col='red')

lines(l_hier, col='green')

predict(clust.hier_raw,newdata=unlist(m3$glucoseValue))

[1] 2

predict(clust.hier_raw,newdata=unlist(m6$glucoseValue))

[1] 3

predict(clust.hier_raw,newdata=unlist(m9$glucoseValue))

[1] 3

predict(clust.hier_raw,newdata=unlist(m12$glucoseValue))

[1] 4

index_hier_raw=arandi(l_hier,m_hier)
unadjusted_hier_raw=arandi(l_hier,m_hier,adjust=FALSE)
index_hier_raw

[1] 0.3966245

unadjusted_hier_raw

[1] 0.8030303

=========== Partitional => Raw ===========

clust.par_raw <- tsclust(training_frame, type = "partitional", k = 4L, distance = "dtw2", trace=TRUE)

    Precomputing distance matrix...

Iteration 1: Changes / Distsum = 12 / 659.1767
Iteration 2: Changes / Distsum =  1 / 489.335
Iteration 3: Changes / Distsum =  0 / 489.335

    Elapsed time is 62.314 seconds.

plot(clust.par_raw, type="sc")

t(cbind(training_frame[,0], cluster = clust.par_raw@cluster))

        [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
cluster    3    3    4    2    3    2    3    1    2     1     1     1

l_par <- clust.par_raw@cluster
m_par <- c(4,4,4,1,1,1,2,2,2,3,3,3)

plot(range(1:12),range(1:4), type='n')
points(m_par, col='red')

lines(l_par, col='green')

predict(clust.par_raw,newdata=unlist(m3$glucoseValue))

[1] 4

predict(clust.par_raw,newdata=unlist(m6$glucoseValue))

[1] 1

predict(clust.par_raw,newdata=unlist(m9$glucoseValue))

[1] 2

predict(clust.par_raw,newdata=unlist(m12$glucoseValue))

[1] 1

index_par_raw=arandi(l_par,m_par)
unadjusted_par_raw=arandi(l_par,m_par,adjust=FALSE)
index_par_raw

[1] 0.2109705

unadjusted_par_raw

[1] 0.7424242

========== K Means => Raw ===========

kmeans_cluster_raw = KMeans_rcpp(training_frame,clusters=4)
l_kmeans_raw = kmeans_cluster_raw$cluster
l_kmeans_raw

 [1] 3 4 1 2 2 4 3 4 2 4 4 4

m_kmeans_raw <- c(1,1,1,2,2,2,3,3,3,4,4,4)

index_kmeans_raw = arandi(l_kmeans_raw,m_kmeans_raw)
unadjusted_kmeans_raw = arandi(l_kmeans_raw,m_kmeans_raw,adjust = TRUE)
index_kmeans_raw

[1] 0.04528302

unadjusted_kmeans_raw

[1] 0.04528302

========== Linear Scaling ============

linearScaling = function(data){
  scaled = c()
  for (i in 1:length(data)) {
    scaled[i] = (data[i]-min(data))/(max(data)-min(data))
    #print(scaled[i])
  }
  return(scaled)
}


training_frame_scaled<-rbind.data.frame(
  linearScaling(m1$glucoseValue),
  linearScaling(m2$glucoseValue),
  linearScaling(m3$glucoseValue),
  linearScaling(m4$glucoseValue),
  linearScaling(m5$glucoseValue),
  linearScaling(m6$glucoseValue),
  linearScaling(m7$glucoseValue),
  linearScaling(m8$glucoseValue),
  linearScaling(m9$glucoseValue),
  linearScaling(m10$glucoseValue),
  linearScaling(m11$glucoseValue),
  linearScaling(m12$glucoseValue)
)
training_frame_scaled

======== HIERARCHICAL => Scaled ===========

clust.hier_scaled <- tsclust(training_frame_scaled, type = "h", k = 4L, distance = "dtw2", trace=TRUE, control = hierarchical_control(method = "ward.D"))

Calculating distance matrix...
Performing hierarchical clustering...
Extracting centroids...

    Elapsed time is 100.308 seconds.

plot(clust.hier_scaled, type="sc")

plot(clust.hier_scaled)

t(cbind(training_frame_scaled[,0], cluster = clust.hier_scaled@cluster))

        1 2 3 4 5 6 7 8 9 10 11 12
cluster 1 2 3 4 2 4 2 4 3  4  4  4

l_hier <- clust.hier_scaled@cluster
m_hier <- c(1,1,1,3,3,3,2,2,2,4,4,4)

plot(range(1:12),range(1:4), type='n')
points(m_hier, col='red')

lines(l_hier, col='green')

predict(clust.hier_scaled,newdata=unlist(linearScaling(m3$glucoseValue)))

[1] 3

predict(clust.hier_scaled,newdata=unlist(linearScaling(m6$glucoseValue)))

[1] 3

predict(clust.hier_scaled,newdata=unlist(linearScaling(m9$glucoseValue)))

[1] 3

predict(clust.hier_scaled,newdata=unlist(linearScaling(m12$glucoseValue)))

[1] 3

index_hier_scaled=arandi(l_hier,m_hier)
unadjusted_hier_scaled=arandi(l_hier,m_hier,adjust=FALSE)
index_hier_scaled

[1] 0.04528302

unadjusted_hier_scaled

[1] 0.6515152

=========== Partitional => Scaled ===========

clust.par_scaled <- tsclust(training_frame_scaled, type = "partitional", k = 4L, distance = "dtw2", trace=TRUE)

    Precomputing distance matrix...

Iteration 1: Changes / Distsum = 12 / 37.12822
Iteration 2: Changes / Distsum = 1 / 32.85783
Iteration 3: Changes / Distsum = 0 / 32.85783

    Elapsed time is 54.819 seconds.

plot(clust.par_scaled, type="sc")

t(cbind(training_frame_scaled[,0], cluster = clust.par_scaled@cluster))

        [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
cluster    3    2    4    1    2    1    2    1    2     1     4     4

l_par <- clust.par_scaled@cluster
m_par <- c(3,3,3,1,1,1,2,2,2,4,4,4)

plot(range(1:12),range(1:4), type='n')
points(m_par, col='red')

lines(l_par, col='green')

predict(clust.par_raw,newdata=unlist(linearScaling(m3$glucoseValue)))

[1] 1

predict(clust.par_raw,newdata=unlist(linearScaling(m6$glucoseValue)))

[1] 1

predict(clust.par_raw,newdata=unlist(linearScaling(m9$glucoseValue)))

[1] 1

predict(clust.par_raw,newdata=unlist(linearScaling(m12$glucoseValue)))

[1] 1

index_par_scaled=arandi(l_par,m_par)
unadjusted_par_scaled=arandi(l_par,m_par,adjust=FALSE)
index_par_scaled

[1] 0.02531646

unadjusted_par_scaled

[1] 0.6818182

========== K Means => Scaled ===========

kmeans_cluster_scaled = KMeans_rcpp(training_frame_scaled,clusters=4)
l_kmeans_scaled = kmeans_cluster_scaled$cluster
l_kmeans_scaled

 [1] 3 2 3 1 2 1 3 4 4 2 1 4

m_kmeans_scaled <- c(3,3,3,2,2,2,4,4,4,1,1,1)

index_kmeans_scaled = arandi(l_kmeans_scaled,m_kmeans_scaled)
unadjusted_kmeans_scaled = arandi(l_kmeans_scaled,m_kmeans_scaled,adjust = TRUE)
index_kmeans_scaled

[1] 0.08333333

unadjusted_kmeans_scaled

[1] 0.08333333

=========== Z Score Normalization =============

training_frame_zscore<-rbind.data.frame(
  zscore(m1$glucoseValue),
  zscore(m2$glucoseValue),
  zscore(m3$glucoseValue),
  zscore(m4$glucoseValue),
  zscore(m5$glucoseValue),
  zscore(m6$glucoseValue),
  zscore(m7$glucoseValue),
  zscore(m8$glucoseValue),
  zscore(m9$glucoseValue),
  zscore(m10$glucoseValue),
  zscore(m11$glucoseValue),
  zscore(m12$glucoseValue)
)
training_frame_zscore

======== HIERARCHICAL => Z score ===========

clust.hier_zscore <- tsclust(training_frame_zscore, type = "h", k = 4L, distance = "dtw2", trace=TRUE, control = hierarchical_control(method = "ward.D"))

Calculating distance matrix...
Performing hierarchical clustering...
Extracting centroids...

    Elapsed time is 139.071 seconds.

plot(clust.hier_zscore, type="sc")

plot(clust.hier_zscore)

t(cbind(training_frame_zscore[,0], cluster = clust.hier_zscore@cluster))

        1 2 3 4 5 6 7 8 9 10 11 12
cluster 1 1 2 3 1 3 1 3 4  4  4  2

l_hier <- clust.hier_zscore@cluster
m_hier <- c(1,1,1,3,3,3,2,2,2,4,4,4)

plot(range(1:12),range(1:4), type='n')
points(m_hier, col='red')

lines(l_hier, col='green')

predict(clust.hier_zscore,newdata=unlist(zscore(m3$glucoseValue)))

[1] 2

predict(clust.hier_zscore,newdata=unlist(zscore(m6$glucoseValue)))

[1] 2

predict(clust.hier_zscore,newdata=unlist(zscore(m9$glucoseValue)))

[1] 4

predict(clust.hier_zscore,newdata=unlist(zscore(m12$glucoseValue)))

[1] 2

index_hier_zscore=arandi(l_hier,m_hier)
unadjusted_hier_zscore=arandi(l_hier,m_hier,adjust=FALSE)
index_hier_zscore

[1] 0.06278027

unadjusted_hier_zscore

[1] 0.7121212

=========== Partitional => Zscore ===========

clust.par_zscore <- tsclust(training_frame_zscore, type = "partitional", k = 4L, distance = "dtw2", trace=TRUE)

    Precomputing distance matrix...

Iteration 1: Changes / Distsum = 12 / 199.0184
Iteration 2: Changes / Distsum = 2 / 192.3014
Iteration 3: Changes / Distsum = 1 / 161.7827
Iteration 4: Changes / Distsum = 0 / 161.7827

    Elapsed time is 69.23 seconds.

plot(clust.par_zscore, type="sc")

t(cbind(training_frame_zscore[,0], cluster = clust.par_zscore@cluster))

        [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
cluster    3    2    1    1    2    1    2    1    2     4     4     1

l_par <- clust.par_zscore@cluster
m_par <- c(3,3,3,1,1,1,2,2,2,4,4,4)

plot(range(1:12),range(1:4), type='n')
points(m_par, col='red')

lines(l_par, col='green')

predict(clust.par_zscore,newdata=unlist(zscore(m3$glucoseValue)))

[1] 1

predict(clust.par_zscore,newdata=unlist(zscore(m6$glucoseValue)))

[1] 2

predict(clust.par_zscore,newdata=unlist(zscore(m9$glucoseValue)))

[1] 2

predict(clust.par_zscore,newdata=unlist(zscore(m12$glucoseValue)))

[1] 1

index_par_zscore=arandi(l_par,m_par)
unadjusted_par_zscore=arandi(l_par,m_par,adjust=FALSE)
index_par_zscore

[1] -0.007968127

unadjusted_par_zscore

[1] 0.6515152

========== K Means => Zscore ===========

kmeans_cluster_zscore = KMeans_rcpp(training_frame_zscore,clusters=4)
l_kmeans_zscore = kmeans_cluster_zscore$cluster
l_kmeans_zscore

 [1] 3 2 2 1 2 1 3 4 2 2 1 4

m_kmeans_zscore <- c(2,2,2,3,3,3,4,4,4,1,1,1)

index_kmeans_zscore = arandi(l_kmeans_scaled,m_kmeans_scaled)
unadjusted_kmeans_zscore = arandi(l_kmeans_scaled,m_kmeans_scaled,adjust = TRUE)
index_kmeans_zscore

[1] 0.08333333

unadjusted_kmeans_zscore

[1] 0.08333333

=========Plotting============

colors = c('red','blue','green')
c_types = c('Hierarchical Clustering','Partitional Clustering','K Means Clustering')
t_types = c('Raw', 'Min-Max Scaling','Z-Score Normalization')

index_all = c(index_hier_raw,index_hier_scaled,index_hier_zscore, index_par_raw, index_par_scaled, index_par_zscore, index_kmeans_raw, index_kmeans_scaled, index_kmeans_zscore)

plot(index_all,xaxt='n',type='h', col = colors, xlab="Clustering Methods", ylab="Adjusted Random Index", main='Adjusted Random Index: Hierarchical, Partitional & K-Means Clustering', lwd=4)
axis(1, at=seq(2,9,by=3), labels=c_types[1:3])

legend("topright",t_types, col = colors, title = 'Transformation Types', lwd=2,cex=.75)

unadjusted_all = c(unadjusted_hier_raw, unadjusted_hier_scaled, unadjusted_hier_zscore, unadjusted_par_raw, unadjusted_par_scaled, unadjusted_par_zscore, unadjusted_kmeans_raw, unadjusted_kmeans_scaled, unadjusted_kmeans_zscore)

plot(unadjusted_all,xaxt='n',type='h', col = colors, xlab="Clustering Methods", ylab="Unadjusted Random Index", main='Unadjusted Index: Hierarchical, Partitional & K-Means Clustering', lwd=4)
axis(1, at=seq(2,9,by=3), labels=c_types[1:3])

legend("topright",t_types, col = colors, title = 'Transformation Types', lwd=2, cex=0.75)